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84
Learning to detect unseen object classes by betweenclass attribute transfer
 In CVPR
, 2009
"... We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied in computer vision research, but it is the rule rather than the exception, because the world contains tens of t ..."
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Cited by 169 (4 self)
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We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied in computer vision research, but it is the rule rather than the exception, because the world contains tens of thousands of different object classes and for only a very few of them image, collections have been formed and annotated with suitable class labels. In this paper, we tackle the problem by introducing attributebased classification. It performs object detection based on a humanspecified highlevel description of the target objects instead of training images. The description consists of arbitrary semantic attributes, like shape, color or even geographic information. Because such properties transcend the specific learning task at hand, they can be prelearned, e.g. from image datasets unrelated to the current task. Afterwards, new classes can be detected based on their attribute representation, without the need for a new training phase. In order to evaluate our method and to facilitate research in this area, we have assembled a new largescale dataset, “Animals with Attributes”, of over 30,000 animal images that match the 50 classes in Osherson’s classic table of how strongly humans associate 85 semantic attributes with animal classes. Our experiments show that by using an attribute layer it is indeed possible to build a learning object detection system that does not require any training images of the target classes. 1.
Symbolic Analysis for Parallelizing Compilers
, 1994
"... Symbolic Domain The objects in our abstract symbolic domain are canonical symbolic expressions. A canonical symbolic expression is a lexicographically ordered sequence of symbolic terms. Each symbolic term is in turn a pair of an integer coefficient and a sequence of pairs of pointers to program va ..."
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Cited by 109 (4 self)
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Symbolic Domain The objects in our abstract symbolic domain are canonical symbolic expressions. A canonical symbolic expression is a lexicographically ordered sequence of symbolic terms. Each symbolic term is in turn a pair of an integer coefficient and a sequence of pairs of pointers to program variables in the program symbol table and their exponents. The latter sequence is also lexicographically ordered. For example, the abstract value of the symbolic expression 2ij+3jk in an environment that i is bound to (1; (( " i ; 1))), j is bound to (1; (( " j ; 1))), and k is bound to (1; (( " k ; 1))) is ((2; (( " i ; 1); ( " j ; 1))); (3; (( " j ; 1); ( " k ; 1)))). In our framework, environment is the abstract analogous of state concept; an environment is a function from program variables to abstract symbolic values. Each environment e associates a canonical symbolic value e x for each variable x 2 V ; it is said that x is bound to e x. An environment might be represented by...
Testing for a signal with unknown location and scale in a stationary Gaussian random field
, 1995
"... this paper are concerned with approximate evaluation of the significance level of the test defined by (1.5), i.e., the probability when = 0 that X max exceeds a constant threshold, say b. First order approximations for this can easily be derived from the results going back to Belyaev and Pitaberg ( ..."
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Cited by 59 (20 self)
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this paper are concerned with approximate evaluation of the significance level of the test defined by (1.5), i.e., the probability when = 0 that X max exceeds a constant threshold, say b. First order approximations for this can easily be derived from the results going back to Belyaev and Pitaberg (1972) (see Adler, 1981, Theorem 6.9.1, p. 160) who give the the following. Suppose Y (r) is a zero mean, unit variance, stationary random field defined on an interval S ae IR
Boundary corrections for the expected Euler characteristic of excursion sets of random fields, with an application to astrophysics
 Advances in Applied Probability
, 1995
"... Certain images arising in astrophysics and medicine are modelled as smooth random fields inside a fixed region, and experimenters are interested in the number of ‘peaks’, or more generally, the topological structure of ‘hotspots ’ present in such an image. This paper studies the Euler characteristi ..."
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Cited by 41 (16 self)
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Certain images arising in astrophysics and medicine are modelled as smooth random fields inside a fixed region, and experimenters are interested in the number of ‘peaks’, or more generally, the topological structure of ‘hotspots ’ present in such an image. This paper studies the Euler characteristic of the excursion set of a random field; the excursion set is the set of points where the image exceeds a fixed threshold, and the Euler characteristic counts the number of connected components in the excursion set minus the number of ‘holes’. For high thresholds the Euler characteristic is a measure of the number of peaks. The geometry of excursion sets has been studied by Adler (1981) who gives the expectation of two excursion set characteristics, called the DT (differential topology) and IG (integral geometry) characteristics, which equal the Euler characteristic provided the excursion set does not touch the boundary of the region. Worsley (1994) finds a boundary correction which gives the expectation of the Euler characteristic itself in two and three dimensions. The proof uses a representation of the Euler characteristic given by Hadwiger (1959). The purpose of this paper is to give a general result for any number of dimensions. The proof takes a different approach and uses a representation from Morse theory. Results are applied to the recently discovered anomolies in the cosmic microwave background radiation, thought to be the remnants of the creation of the universe.
Graph Colorings and Related Symmetric Functions: Ideas and Applications
, 1998
"... this paper we will report on further work related to this symmetric function ..."
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Cited by 35 (2 self)
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this paper we will report on further work related to this symmetric function
User interface design with matrix algebra
 ACM Transactions on CHI
, 2004
"... It is usually very hard, both for designers and users, to reason reliably about user interfaces. This article shows that ‘push button ’ and ‘point and click ’ user interfaces are algebraic structures. Users effectively do algebra when they interact, and therefore we can be precise about some importa ..."
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Cited by 26 (11 self)
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It is usually very hard, both for designers and users, to reason reliably about user interfaces. This article shows that ‘push button ’ and ‘point and click ’ user interfaces are algebraic structures. Users effectively do algebra when they interact, and therefore we can be precise about some important design issues and issues of usability. Matrix algebra, in particular, is useful for explicit calculation and for proof of various user interface properties. With matrix algebra, we are able to undertake with ease unusally thorough reviews of real user interfaces: this article examines a mobile phone, a handheld calculator and a digital multimeter as case studies, and draws general conclusions about the approach and its relevance to design.
Handling FloatingPoint Exceptions in Numeric Programs
 ACM Transactions on Programming Languages and Systems
, 1996
"... Language Constructs Termination exception mechanisms like in Ada and C++ are supposed to terminate an unsuccessful computation as soon as possible after an exception occurs. However, none of the examples of numeric exception handling presented earlier depends ACM Transactions on Programming Languag ..."
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Cited by 24 (0 self)
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Language Constructs Termination exception mechanisms like in Ada and C++ are supposed to terminate an unsuccessful computation as soon as possible after an exception occurs. However, none of the examples of numeric exception handling presented earlier depends ACM Transactions on Programming Languages and Systems, Vol. 18, No. 2, March 1996. Handling FloatingPoint Exceptions 167 on the immediate termination of a calculation signaling an exception. The IEEE exception flags scheme actually takes advantage of the fact that an immediate jump is not necessary; by raising a flag, making a substitution, and continuing, the IEEE Standard supports both an attempted/alternate form and a default substitution with a single, simple reponse to exceptions. A detraction of the IEEE flag solution, though, is its obvious lack of structure. Instead of being forced to set and reset flags, one would ideally have available a language construct that more directly reflected the attempted/alternate algorit...
The CacheOblivious Gaussian Elimination Paradigm: Theoretical Framework, Parallelization and Experimental Evaluation
, 2009
"... We consider triplynested loops of the type that occur in the standard Gaussian elimination algorithm, which we denote by GEP (or the Gaussian Elimination Paradigm). We present two related cacheoblivious methods IGEP and CGEP, both of which reduce the number of I/Os performed by the computation o ..."
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Cited by 21 (7 self)
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We consider triplynested loops of the type that occur in the standard Gaussian elimination algorithm, which we denote by GEP (or the Gaussian Elimination Paradigm). We present two related cacheoblivious methods IGEP and CGEP, both of which reduce the number of I/Os performed by the computation over that performed by standard GEP by a factor of √ M, where M is the size of the cache. Cacheoblivious IGEP computes inplace and solves most of the known applications of GEP including Gaussian elimination and LUdecomposition without pivoting and FloydWarshall allpairs shortest paths. Cacheoblivious CGEP uses a modest amount of additional space, but is completely general and applies to any code in GEP form. Both IGEP and CGEP produce systemindependent cacheefficient code, and are potentially applicable to being used by optimizing compilers for loop transformation. We present parallel IGEP and CGEP that achieve good speedup and match the sequential caching performance cacheobliviously for both shared and distributed caches for sufficiently large inputs. We present extensive experimental results for both incore and outofcore performance of our algorithms. We consider both sequential and parallel implementations, and compare them with finelytuned cacheaware BLAS code for matrix multiplication and Gaussian elimination without pivoting. Our results indicate that cacheoblivious GEP offers an attractive tradeoff between efficiency and portability.
Convolution polynomials
 The Mathematica Journal 2,4 (Fall
, 1992
"... Abstract. The polynomials that arise as coefficients when a power series is raised to the power x include many important special cases, which have surprising properties that are not widely known. This paper explains how to recognize and use such properties, and it closes with a general result about ..."
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Cited by 21 (1 self)
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Abstract. The polynomials that arise as coefficients when a power series is raised to the power x include many important special cases, which have surprising properties that are not widely known. This paper explains how to recognize and use such properties, and it closes with a general result about approximating such polynomials asymptotically. A family of polynomials F0(x),F1(x),F2(x),... forms a convolution family if Fn(x) has degree ≤ n and if the convolution condition Fn(x + y) = Fn(x)F0(y) + Fn−1(x)F1(y) + · · · + F1(x)Fn−1(y) + F0(x)Fn(y) holds for all x and y and for all n ≥ 0. Many such families are known, and they appear frequently in applications. For example, we can let Fn(x) = x n /n!; the condition (x + y) n n! n∑ k=0 x k k! y n−k (n − k)! is equivalent to the binomial theorem for integer exponents. Or we can let Fn(x) be the binomial coefficient () x n; the corresponding identity ( ) n∑ x + y x y n k n − k is commonly called Vandermonde’s convolution. k=0 How special is the convolution condition? Mathematica will readily find all sequences of polynomials that work for, say, 0 ≤ n ≤ 4: F[n_,x_]:=Sum[f[n,j]x^j,{j,0,n}]/n! conv[n_]:=LogicalExpand[Series[F[n,x+y],{x,0,n},{y,0,n}]